A clinically actionable and explainable real-time risk assessment framework for stroke-associated pneumonia.

The current medical practice is more responsive rather than proactive, despite the widely recognized value of early disease detection, including improving the quality of care and reducing medical costs. One of the cornerstones of early disease detection is clinically actionable predictions, where predictions are expected to be accurate, stable, real-time and interpretable. As an example, we used stroke-associated pneumonia (SAP), setting up a transformer-encoder-based model that analyzes highly heterogeneous electronic health records in real-time.

Adherence to Stroke Care Performance Measures in Different Regions in China, 2015-2019: Evidence From the Chinese Stroke Center Alliance.

Background And Objectives: To explore the regional discrepancy of the adherence to guideline-recommended stroke interventions for the stroke belt division (north vs south), the economic development division (east vs middle vs west), and potential interaction.

Methods: We conducted a retrospective observational study using data from the Chinese Stroke Center Alliance from August 2015 to August 2019. The primary outcome was hospital personnel adherence to 11 individual guideline-recommended treatments.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application.

Ambulatory blood pressure profile and stroke recurrence.

Objectives: To establish a new ambulatory blood pressure (ABP) parameter (24-hour ABP profile) and evaluated its performance on stroke outcome in ischaemic stroke (IS) or transient ischaemic attack (TIA) patients.

Methods: The prospective cohort consisted of 1996 IS/TIA patients enrolled for ABP monitoring and a 3-month follow-up for stroke recurrence as outcome. Profile groups of systolic blood pressure (SBP) were identified via an advanced functional clustering method, and the associations of the profile groups and conventional ABP parameters with stroke recurrence were examined in a Cox proportional hazards model.

GRP per capita and hospital characteristics associated with intravenous tissue plasminogen activator adherence rate: evidence from the Chinese Stroke Center Alliance.

Background: Timely delivery of intravenous tissue plasminogen activator (IV-rt PA) is pivotal to eligible patients who had a stroke while achieving higher rates of IV-rt PA has been problematic. This paper focuses on investigating influential factors associated with the administration of IV-rt PA, primarily per capita gross regional product (GRP) and healthcare system factors.

Methods: The study included 980 hospitals in the Chinese Stroke Center Alliance where 158 003 patients who had an acute ischaemic stroke received IV-rt PA between August 2015 and August 2019.

Sampling Properties of color Independent Component Analysis.

Independent Component Analysis (ICA) offers an effective data-driven approach for blind source extraction encountered in many signal and image processing problems. Although many ICA methods have been developed, they have received relatively little attention in the statistics literature, especially in terms of rigorous theoretical investigation for statistical inference. The current paper aims at narrowing this gap and investigates the statistical sampling properties of the colorICA (cICA) method.

Who may benefit from lower dosages of intravenous tissue plasminogen activator? Results from a cluster data analysis.

Background: The risk of symptomatic intracranial haemorrhage (sICH) after thrombolysis is low but severe. Lower dose of alteplase may reduce the risk of sICH. We aim to identify subsets of patients who could benefit from lower dose of alteplase compared with standard dose.

Identification of Long Non-Coding RNA Expression Profiles and Co-Expression Genes in Thyroid Carcinoma Based on The Cancer Genome Atlas (TCGA) Database.

BACKGROUND Thyroid carcinoma is a malignancy with high morbidity and mortality. Genetic alterations play pivot roles in the pathogenesis of thyroid carcinoma, where long noncoding RNA (lncRNA) have been identified to be crucial. This study sought to investigate the biological functions of lncRNA expression profiles in thyroid carcinoma.

The efficacy and safety of nimodipine in acute ischemic stroke patients with mild cognitive impairment: a double-blind, randomized, placebo-controlled trial.

Nimodipine might be effective in subcortical vascular dementia (VaD). Its benefit in preventing further cognitive decline in patients with acute ischemic stroke (AIS) and vascular mild cognitive impairment (VaMCI) remains to be established. In this multicenter, double-blind trial, we randomly assigned 654 eligible patients to nimodipine 30 mg three times a day or placebo.

A survey of high dimension low sample size asymptotics.

Peter Hall's work illuminated many aspects of statistical thought, some of which are very well known including the bootstrap and smoothing. However, he also explored many other lesser known aspects of mathematical statistics. This is a survey of one of those areas, initiated by a seminal paper in 2005, on high dimension low sample size asymptotics.

Effect of a Multifaceted Quality Improvement Intervention on Hospital Personnel Adherence to Performance Measures in Patients With Acute Ischemic Stroke in China: A Randomized Clinical Trial.

Importance: In China and other parts of the world, hospital personnel adherence to evidence-based stroke care is limited.

Objective: To determine whether a multifaceted quality improvement intervention can improve hospital personnel adherence to evidence-based performance measures in patients with acute ischemic stroke (AIS) in China.

Design, Setting, And Participants: A multicenter, cluster-randomized clinical trial among 40 public hospitals in China that enrolled 4800 patients hospitalized with AIS from August 10, 2014, through June 20, 2015, with 12-month follow-up through July 30, 2016.

Exponential Family Functional data analysis via a low-rank model.

In many applications, non-Gaussian data such as binary or count are observed over a continuous domain and there exists a smooth underlying structure for describing such data. We develop a new functional data method to deal with this kind of data when the data are regularly spaced on the continuous domain. Our method, referred to as Exponential Family Functional Principal Component Analysis (EFPCA), assumes the data are generated from an exponential family distribution, and the matrix of the canonical parameters has a low-rank structure.

The network of Shanghai Stroke Service System (4S): A public health-care web-based database using automatic extraction of electronic medical records.

Background Several stroke outcome and quality control projects have demonstrated the success in stroke care quality improvement through structured process. However, Chinese health-care systems are challenged with its overwhelming numbers of patients, limited resources, and large regional disparities. Aim To improve quality of stroke care to address regional disparities through process improvement.

Artificial intelligence in healthcare: past, present and future.

Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future.

Length of Stay, Mortality, Cost, and Perceptions of Care Associated With Transition From an Open to Closed Staffing Model in the Cardiac Intensive Care Unit.

Background: Organizational models in the intensive care unit (ICU) have classically been described as either closed or open, depending on the presence or absence of a dedicated ICU team. Although a closed model has been shown to improve patient outcomes in medical and surgical ICUs, the merits of various care models have not been previously explored in the cardiac ICU (CICU) setting.

Methods: From November 2012 to March 2014, data were prospectively collected on all admissions before and after transition from an open to closed CICU at our institution.

The Statistics and Mathematics of High Dimension Low Sample Size Asymptotics.

The aim of this paper is to establish several deep theoretical properties of principal component analysis for multiple-component spike covariance models. Our new results reveal an asymptotic conical structure in critical sample eigendirections under the spike models with distinguishable (or indistinguishable) eigenvalues, when the sample size and/or the number of variables (or dimension) tend to infinity. The consistency of the sample eigenvectors relative to their population counterparts is determined by the ratio between the dimension and the product of the sample size with the spike size.

Substantial Progress Yet Significant Opportunity for Improvement in Stroke Care in China.

Background And Purpose: Stroke is a leading cause of death in China. Yet the adherence to guideline-recommended ischemic stroke performance metrics in the past decade has been previously shown to be suboptimal. Since then, several nationwide stroke quality management initiatives have been conducted in China.

A SPARSE REDUCED RANK FRAMEWORK FOR GROUP ANALYSIS OF FUNCTIONAL NEUROIMAGING DATA.

In spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain.

The Genome-Wide Influence on Human BMI Depends on Physical Activity, Life Course, and Historical Period.

In this analysis, guided by an evolutionary framework, we investigate how the human genome as a whole interacts with historical period, age, and physical activity to influence body mass index (BMI). The genomic influence is estimated by (1) heritability or the proportion of variance in BMI explained by genome-wide genotype data, and (2) the random effects or the best linear unbiased predictors (BLUPs) of genome-wide association studies (GWAS) data on BMI. Data were used from the Framingham Heart Study (FHS) in the United States.

Rationale and design of a cluster-randomized multifaceted intervention trial to improve stroke care quality in China: The GOLDEN BRIDGE-Acute Ischemic Stroke.

Background: Prior studies have demonstrated a significant gap between guideline-based recommendations and clinical practice in the management of acute ischemic stroke (AIS) in China.

Aims: This study implements a targeted multifaceted quality improvement intervention in AIS patients and identifies the feasibility and efficacy of this intervention.

Design: This is a multicenter, 2-arm, open-label, cluster-randomized trial involving 40 clusters (hospitals) from China National Network of Stroke Research.

Mixture SNPs effect on phenotype in genome-wide association studies.

Background: Recently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects.

Functional Data Analysis of Tree Data Objects.

Data analysis on non-Euclidean spaces, such as tree spaces, can be challenging. The main contribution of this paper is establishment of a connection between tree data spaces and the well developed area of Functional Data Analysis (FDA), where the data objects are curves. This connection comes through two tree representation approaches, the and the .

Risk score to predict gastrointestinal bleeding after acute ischemic stroke.

Background: Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke.